1
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Nowinski WL. Taxonomy of Acute Stroke: Imaging, Processing, and Treatment. Diagnostics (Basel) 2024; 14:1057. [PMID: 38786355 PMCID: PMC11119045 DOI: 10.3390/diagnostics14101057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/01/2024] [Accepted: 05/16/2024] [Indexed: 05/25/2024] Open
Abstract
Stroke management employs a variety of diagnostic imaging modalities, image processing and analysis methods, and treatment procedures. This work categorizes methods for stroke imaging, image processing and analysis, and treatment, and provides their taxonomies illustrated by a state-of-the-art review. Imaging plays a critical role in stroke management, and the most frequently employed modalities are computed tomography (CT) and magnetic resonance (MR). CT includes unenhanced non-contrast CT as the first-line diagnosis, CT angiography, and CT perfusion. MR is the most complete method to examine stroke patients. MR angiography is useful to evaluate the severity of artery stenosis, vascular occlusion, and collateral flow. Diffusion-weighted imaging is the gold standard for evaluating ischemia. MR perfusion-weighted imaging assesses the penumbra. The stroke image processing methods are divided into non-atlas/template-based and atlas/template-based. The non-atlas/template-based methods are subdivided into intensity and contrast transformations, local segmentation-related, anatomy-guided, global density-guided, and artificial intelligence/deep learning-based. The atlas/template-based methods are subdivided into intensity templates and atlases with three atlas types: anatomy atlases, vascular atlases, and lesion-derived atlases. The treatment procedures for arterial and venous strokes include intravenous and intraarterial thrombolysis and mechanical thrombectomy. This work captures the state-of-the-art in stroke management summarized in the form of comprehensive and straightforward taxonomy diagrams. All three introduced taxonomies in diagnostic imaging, image processing and analysis, and treatment are widely illustrated and compared against other state-of-the-art classifications.
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Affiliation(s)
- Wieslaw L Nowinski
- Sano Centre for Computational Personalised Medicine, Czarnowiejska 36, 30-054 Krakow, Poland
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2
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Heo H, Park HY, Suh CH, Shim WH, Lim JS, Lee JH, Kim SJ. Development of statistical auto-segmentation method for diffusion restriction gray matter lesions in patients with newly diagnosed sporadic Creutzfeldt-Jakob disease. Sci Rep 2024; 14:4215. [PMID: 38378772 PMCID: PMC10879176 DOI: 10.1038/s41598-024-51927-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 01/11/2024] [Indexed: 02/22/2024] Open
Abstract
Quantification of diffusion restriction lesions in sporadic Creutzfeldt-Jakob disease (sCJD) may provide information of the disease burden. We aim to develop an automatic segmentation model for sCJD and to evaluate the volume of disease extent as a prognostic marker for overall survival. Fifty-six patients (mean age ± SD, 61.2 ± 9.9 years) were included from February 2000 to July 2020. A threshold-based segmentation was used to obtain abnormal signal intensity masks. Segmented volumes were compared with the visual grade. The Dice similarity coefficient was calculated to measure the similarity between the automatic vs. manual segmentation. Cox proportional hazards regression analysis was performed to evaluate the volume of disease extent as a prognostic marker. The automatic segmentation showed good correlation with the visual grading. The cortical lesion volumes significantly increased as the visual grade aggravated (extensive: 112.9 ± 73.2; moderate: 45.4 ± 30.4; minimal involvement: 29.6 ± 18.1 mm3) (P < 0.001). The deep gray matter lesion volumes were significantly higher for positive than for negative involvement of the deep gray matter (5.6 ± 4.6 mm3 vs. 1.0 ± 1.3 mm3, P < 0.001). The mean Dice similarity coefficients were 0.90 and 0.94 for cortical and deep gray matter lesions, respectively. However, the volume of disease extent was not associated with worse overall survival (cortical extent: P = 0.07; deep gray matter extent: P = 0.12).
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Affiliation(s)
- Hwon Heo
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - Ho Young Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - Chong Hyun Suh
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea.
| | - Woo Hyun Shim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - Jae-Sung Lim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jae-Hong Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sang Joon Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
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3
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Amador K, Gutierrez A, Winder A, Fiehler J, Wilms M, Forkert ND. Providing clinical context to the spatio-temporal analysis of 4D CT perfusion to predict acute ischemic stroke lesion outcomes. J Biomed Inform 2024; 149:104567. [PMID: 38096945 DOI: 10.1016/j.jbi.2023.104567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 10/25/2023] [Accepted: 12/07/2023] [Indexed: 12/18/2023]
Abstract
Acute ischemic stroke is a leading cause of mortality and morbidity worldwide. Timely identification of the extent of a stroke is crucial for effective treatment, whereas spatio-temporal (4D) Computed Tomography Perfusion (CTP) imaging is playing a critical role in this process. Recently, the first deep learning-based methods that leverage the full spatio-temporal nature of perfusion imaging for predicting stroke lesion outcomes have been proposed. However, clinical information is typically not integrated into the learning process, which may be helpful to improve the tissue outcome prediction given the known influence of various factors (i.e., physiological, demographic, and treatment factors) on lesion growth. Cross-attention, a multimodal fusion strategy, has been successfully used to combine information from multiple sources, but it has yet to be applied to stroke lesion outcome prediction. Therefore, this work aimed to develop and evaluate a novel multimodal and spatio-temporal deep learning model that utilizes cross-attention to combine information from 4D CTP and clinical metadata simultaneously to predict stroke lesion outcomes. The proposed model was evaluated using a dataset of 70 acute ischemic stroke patients, demonstrating significantly improved volume estimates (mean error = 19 ml) compared to a baseline unimodal approach (mean error = 35 ml, p< 0.05). The proposed model allows generating attention maps and counterfactual outcome scenarios to investigate the relevance of clinical variables in predicting stroke lesion outcomes at a patient level, helping to provide a better understanding of the model's decision-making process.
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Affiliation(s)
- Kimberly Amador
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, Canada; Department of Radiology, University of Calgary, Calgary, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Canada.
| | - Alejandro Gutierrez
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, Canada; Department of Radiology, University of Calgary, Calgary, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Canada
| | - Anthony Winder
- Department of Radiology, University of Calgary, Calgary, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Matthias Wilms
- Hotchkiss Brain Institute, University of Calgary, Calgary, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Canada; Departments of Pediatrics and Community Health Sciences, University of Calgary, Calgary, Canada
| | - Nils D Forkert
- Department of Radiology, University of Calgary, Calgary, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Canada; Department of Clinical Neurosciences, University of Calgary, Calgary, Canada
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4
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Evans C, Johnstone A, Zich C, Lee JSA, Ward NS, Bestmann S. The impact of brain lesions on tDCS-induced electric fields. Sci Rep 2023; 13:19430. [PMID: 37940660 PMCID: PMC10632455 DOI: 10.1038/s41598-023-45905-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 10/25/2023] [Indexed: 11/10/2023] Open
Abstract
Transcranial direct current stimulation (tDCS) can enhance motor and language rehabilitation after stroke. Though brain lesions distort tDCS-induced electric field (E-field), systematic accounts remain limited. Using electric field modelling, we investigated the effect of 630 synthetic lesions on E-field magnitude in the region of interest (ROI). Models were conducted for two tDCS montages targeting either primary motor cortex (M1) or Broca's area (BA44). Absolute E-field magnitude in the ROI differed by up to 42% compared to the non-lesioned brain depending on lesion size, lesion-ROI distance, and lesion conductivity value. Lesion location determined the sign of this difference: lesions in-line with the predominant direction of current increased E-field magnitude in the ROI, whereas lesions located in the opposite direction decreased E-field magnitude. We further explored how individualised tDCS can control lesion-induced effects on E-field. Lesions affected the individualised electrode configuration needed to maximise E-field magnitude in the ROI, but this effect was negligible when prioritising the maximisation of radial inward current. Lesions distorting tDCS-induced E-field, is likely to exacerbate inter-individual variability in E-field magnitude. Individualising electrode configuration and stimulator output can minimise lesion-induced variability but requires improved estimates of lesion conductivity. Individualised tDCS is critical to overcome E-field variability in lesioned brains.
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Affiliation(s)
- Carys Evans
- Department for Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - Ainslie Johnstone
- Department for Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Catharina Zich
- Department for Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
- Nuffield Department of Clinical Neurosciences, FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Jenny S A Lee
- Department for Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Nick S Ward
- Department for Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
- The National Hospital for Neurology and Neurosurgery, London, UK
- UCLP Centre for Neurorehabilitation, London, UK
| | - Sven Bestmann
- Department for Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
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5
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Nukovic JJ, Opancina V, Ciceri E, Muto M, Zdravkovic N, Altin A, Altaysoy P, Kastelic R, Velazquez Mendivil DM, Nukovic JA, Markovic NV, Opancina M, Prodanovic T, Nukovic M, Kostic J, Prodanovic N. Neuroimaging Modalities Used for Ischemic Stroke Diagnosis and Monitoring. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1908. [PMID: 38003957 PMCID: PMC10673396 DOI: 10.3390/medicina59111908] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 10/16/2023] [Accepted: 10/26/2023] [Indexed: 11/26/2023]
Abstract
Strokes are one of the global leading causes of physical or mental impairment and fatality, classified into hemorrhagic and ischemic strokes. Ischemic strokes happen when a thrombus blocks or plugs an artery and interrupts or reduces blood supply to the brain tissue. Deciding on the imaging modality which will be used for stroke detection depends on the expertise and availability of staff and the infrastructure of hospitals. Magnetic resonance imaging provides valuable information, and its sensitivity for smaller infarcts is greater, while computed tomography is more extensively used, since it can promptly exclude acute cerebral hemorrhages and is more favorable speed-wise. The aim of this article was to give information about the neuroimaging modalities used for the diagnosis and monitoring of ischemic strokes. We reviewed the available literature and presented the use of computed tomography, CT angiography, CT perfusion, magnetic resonance imaging, MR angiography and MR perfusion for the detection of ischemic strokes and their monitoring in different phases of stroke development.
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Affiliation(s)
- Jasmin J. Nukovic
- Faculty of Pharmacy and Health Travnik, University of Travnik, 72270 Travnik, Bosnia and Herzegovina
- Department of Radiology, General Hospital Novi Pazar, 36300 Novi Pazar, Serbia
| | - Valentina Opancina
- Department of Radiology, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia
- Diagnostic Imaging and Interventional Neuroradiology Unit, Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy
- Diagnostic and Interventional Neuroradiology Unit, A.O.R.N. Cardarelli, 80131 Naples, Italy
| | - Elisa Ciceri
- Diagnostic Imaging and Interventional Neuroradiology Unit, Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy
| | - Mario Muto
- Diagnostic and Interventional Neuroradiology Unit, A.O.R.N. Cardarelli, 80131 Naples, Italy
| | - Nebojsa Zdravkovic
- Department of Biomedical Statistics and Informatics, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia
| | - Ahmet Altin
- Faculty of Medicine, Dokuz Eylul University, Izmir 35340, Turkey
| | - Pelin Altaysoy
- Faculty of Medicine, Bahcesehir University, Istanbul 34349, Turkey
| | - Rebeka Kastelic
- Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia
| | | | - Jusuf A. Nukovic
- Faculty of Pharmacy and Health Travnik, University of Travnik, 72270 Travnik, Bosnia and Herzegovina
- Department of Radiology, General Hospital Novi Pazar, 36300 Novi Pazar, Serbia
| | - Nenad V. Markovic
- Department of Surgery, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia
| | - Miljan Opancina
- Department of Biomedical Statistics and Informatics, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia
- Military Medical Academy, Faculty of Medicine, University of Defense, 11000 Belgrade, Serbia
| | - Tijana Prodanovic
- Department of Pediatrics, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia
| | - Merisa Nukovic
- Department of Radiology, General Hospital Novi Pazar, 36300 Novi Pazar, Serbia
| | - Jelena Kostic
- Department of Radiology, Medical Faculty, University of Belgrade, 11120 Beograd, Serbia
| | - Nikola Prodanovic
- Department of Surgery, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia
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6
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An J, Wendt L, Wiese G, Herold T, Rzepka N, Mueller S, Koch SP, Hoffmann CJ, Harms C, Boehm-Sturm P. Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images. Sci Rep 2023; 13:13341. [PMID: 37587160 PMCID: PMC10432383 DOI: 10.1038/s41598-023-39826-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 07/31/2023] [Indexed: 08/18/2023] Open
Abstract
Magnetic resonance imaging (MRI) is widely used for ischemic stroke lesion detection in mice. A challenge is that lesion segmentation often relies on manual tracing by trained experts, which is labor-intensive, time-consuming, and prone to inter- and intra-rater variability. Here, we present a fully automated ischemic stroke lesion segmentation method for mouse T2-weighted MRI data. As an end-to-end deep learning approach, the automated lesion segmentation requires very little preprocessing and works directly on the raw MRI scans. We randomly split a large dataset of 382 MRI scans into a subset (n = 293) to train the automated lesion segmentation and a subset (n = 89) to evaluate its performance. We compared Dice coefficients and accuracy of lesion volume against manual segmentation, as well as its performance on an independent dataset from an open repository with different imaging characteristics. The automated lesion segmentation produced segmentation masks with a smooth, compact, and realistic appearance that are in high agreement with manual segmentation. We report dice scores higher than the agreement between two human raters reported in previous studies, highlighting the ability to remove individual human bias and standardize the process across research studies and centers.
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Affiliation(s)
- Jeehye An
- Department of Experimental Neurology and Center for Stroke Research, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Charité-Universitätsmedizin Berlin, NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Berlin, Germany
| | - Leo Wendt
- Scalable Minds GmbH, Potsdam, Germany
| | | | | | | | - Susanne Mueller
- Department of Experimental Neurology and Center for Stroke Research, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Charité-Universitätsmedizin Berlin, NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Berlin, Germany
| | - Stefan Paul Koch
- Department of Experimental Neurology and Center for Stroke Research, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Charité-Universitätsmedizin Berlin, NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Berlin, Germany
| | - Christian J Hoffmann
- Department of Experimental Neurology and Center for Stroke Research, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health (BIH), Berlin, Germany
| | - Christoph Harms
- Department of Experimental Neurology and Center for Stroke Research, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- German Center for Cardiovascular Research (DZHK), Berlin, Germany
- Einstein Center for Neuroscience, Berlin, Germany
- NeuroCure Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Philipp Boehm-Sturm
- Department of Experimental Neurology and Center for Stroke Research, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
- Charité-Universitätsmedizin Berlin, NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Berlin, Germany.
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7
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Chen IE, Tsui B, Zhang H, Qiao JX, Hsu W, Nour M, Salamon N, Ledbetter L, Polson J, Arnold C, BahrHossieni M, Jahan R, Duckwiler G, Saver J, Liebeskind D, Nael K. Automated estimation of ischemic core volume on noncontrast-enhanced CT via machine learning. Interv Neuroradiol 2022:15910199221145487. [PMID: 36572984 DOI: 10.1177/15910199221145487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Accurate estimation of ischemic core on baseline imaging has treatment implications in patients with acute ischemic stroke (AIS). Machine learning (ML) algorithms have shown promising results in estimating ischemic core using routine noncontrast computed tomography (NCCT). OBJECTIVE We used an ML-trained algorithm to quantify ischemic core volume on NCCT in a comparative analysis to pretreatment magnetic resonance imaging (MRI) diffusion-weighted imaging (DWI) in patients with AIS. METHODS Patients with AIS who had both pretreatment NCCT and MRI were enrolled. An automatic segmentation ML approach was applied using Brainomix software (Oxford, UK) to segment the ischemic voxels and calculate ischemic core volume on NCCT. Ischemic core volume was also calculated on baseline MRI DWI. Comparative analysis was performed using Bland-Altman plots and Pearson correlation. RESULTS A total of 72 patients were included. The time-to-stroke onset time was 134.2/89.5 minutes (mean/median). The time difference between NCCT and MRI was 64.8/44.5 minutes (mean/median). In patients who presented within 1 hour from stroke onset, the ischemic core volumes were significantly (p = 0.005) underestimated by ML-NCCT. In patients presented beyond 1 hour, the ML-NCCT estimated ischemic core volumes approximated those obtained by MRI-DWI and with significant correlation (r = 0.56, p < 0.001). CONCLUSION The ischemic core volumes calculated by the described ML approach on NCCT approximate those obtained by MRI in patients with AIS who present beyond 1 hour from stroke onset.
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Affiliation(s)
- Iris E Chen
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA
| | - Brian Tsui
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA
| | - Haoyue Zhang
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA
| | - Joe X Qiao
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA
| | - William Hsu
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA
| | - May Nour
- Department of Neurology, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA
| | - Luke Ledbetter
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA
| | - Jennifer Polson
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA
| | - Corey Arnold
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA
| | - Mersedeh BahrHossieni
- Department of Neurology, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA
| | - Reza Jahan
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA
| | - Gary Duckwiler
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA
| | - Jeffrey Saver
- Department of Neurology, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA
| | - David Liebeskind
- Department of Neurology, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA
| | - Kambiz Nael
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA
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8
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Fully automatic identification of post-treatment infarct lesions after endovascular therapy based on non-contrast computed tomography. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-08094-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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9
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Kaothanthong N, Atsavasirilert K, Sarampakhul S, Chantangphol P, Songsaeng D, Makhanov S. Artificial intelligence for localization of the acute ischemic stroke by non-contrast computed tomography. PLoS One 2022; 17:e0277573. [PMID: 36454916 PMCID: PMC9714826 DOI: 10.1371/journal.pone.0277573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 10/29/2022] [Indexed: 12/03/2022] Open
Abstract
A non-contrast cranial computer tomography (ncCT) is often employed for the diagnosis of the early stage of the ischemic stroke. However, the number of false negatives is high. More accurate results are obtained by an MRI. However, the MRI is not available in every hospital. Moreover, even if it is available in the clinic for the routine tests, emergency often does not have it. Therefore, this paper proposes an end-to-end framework for detection and segmentation of the brain infarct on the ncCT. The computer tomography perfusion (CTp) is used as the ground truth. The proposed ensemble model employs three deep convolution neural networks (CNNs) to process three end-to-end feature maps and a hand-craft features characterized by specific contra-lateral features. To improve the accuracy of the detected infarct area, the spatial dependencies between neighboring slices are employed at the postprocessing step. The numerical experiments have been performed on 18 ncCT-CTp paired stroke cases (804 image-pairs). The leave-one-out approach is applied for evaluating the proposed method. The model achieves 91.16% accuracy, 65.15% precision, 77.44% recall, 69.97% F1 score, and 0.4536 IoU.
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Affiliation(s)
- Natsuda Kaothanthong
- Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand
| | - Kamin Atsavasirilert
- Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand
| | - Soawapot Sarampakhul
- Division of Diagnostic Radiology, Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Pantid Chantangphol
- Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand
| | - Dittapong Songsaeng
- Division of Diagnostic Radiology, Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Stanislav Makhanov
- Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand
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10
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Amador K, Wilms M, Winder A, Fiehler J, Forkert ND. Predicting treatment-specific lesion outcomes in acute ischemic stroke from 4D CT perfusion imaging using spatio-temporal convolutional neural networks. Med Image Anal 2022; 82:102610. [PMID: 36103772 DOI: 10.1016/j.media.2022.102610] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 07/19/2022] [Accepted: 08/25/2022] [Indexed: 12/30/2022]
Abstract
For the diagnosis and precise treatment of acute ischemic stroke, predicting the final location and volume of lesions is of great clinical interest. Current deep learning-based prediction methods mainly use perfusion parameter maps, which can be calculated from spatio-temporal (4D) CT perfusion (CTP) imaging data, to estimate the tissue outcome of an acute ischemic stroke. However, this calculation relies on a deconvolution operation, an ill-posed problem requiring strong regularization and definition of an arterial input function. Thus, improved predictions might be achievable if the deep learning models were applied directly to acute 4D CTP data rather than perfusion maps. In this work, a novel deep spatio-temporal convolutional neural network is proposed for predicting treatment-dependent stroke lesion outcomes by making full use of raw 4D CTP data. By merging a U-Net-like architecture with temporal convolutional networks, we efficiently process the spatio-temporal information available in CTP datasets to make a tissue outcome prediction. The proposed method was evaluated on 147 patients using a 10-fold cross validation, which demonstrated that the proposed 3D+time model (mean Dice=0.45) significantly outperforms both a 2D+time variant of our approach (mean Dice=0.43) and a state-of-the-art method that uses perfusion maps (mean Dice=0.38). These results show that 4D CTP datasets include more predictive information than perfusion parameter maps, and that the proposed method is an efficient approach to make use of this complex data.
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Affiliation(s)
- Kimberly Amador
- Department of Biomedical Engineering, University of Calgary, Calgary, Canada; Department of Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada.
| | - Matthias Wilms
- Department of Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Canada
| | - Anthony Winder
- Department of Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Nils D Forkert
- Department of Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Canada; Department of Clinical Neurosciences, University of Calgary, Calgary, Canada
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11
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Liu L, Zhang P, Liang G, Xiong S, Wang J, Zheng G. A spatiotemporal correlation deep learning network for brain penumbra disease. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.11.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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12
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Towards an Accurate MRI Acute Ischemic Stroke Lesion Segmentation Based on Bioheat Equation and U-Net Model. Int J Biomed Imaging 2022; 2022:5529726. [PMID: 35880140 PMCID: PMC9308529 DOI: 10.1155/2022/5529726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 08/11/2021] [Accepted: 07/04/2022] [Indexed: 11/29/2022] Open
Abstract
Acute ischemic stroke represents a cerebrovascular disease, for which it is practical, albeit challenging to segment and differentiate infarct core from salvageable penumbra brain tissue. Ischemic stroke causes the variation of cerebral blood flow and heat generation due to metabolism. Therefore, the temperature is modified in the ischemic stroke region. In this paper, we incorporate acute ischemic stroke temperature profile to reinforce segmentation accuracy in MRI. Pennes bioheat equation was used to generate brain thermal images that may provide rich information regarding the temperature change in acute ischemic stroke lesions. The thermal images were generated by calculating the temperature of the brain with acute ischemic stroke. Then, U-Net was used in this paper for the segmentation of acute ischemic stroke. A dataset of 3192 images was created to train U-Net using k-fold crossvalidation. The training time was about 10 hours and 35 minutes in NVIDIA GPU. Next, the obtained trained model was compared with recent methods to analyze the effect of the ischemic stroke temperature profile in segmentation. The obtained results show that significant parts of acute ischemic stroke and background areas are segmented only in thermal images, which proves the importance of using thermal information to improve the segmentation outcomes in MRI diagnosis.
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13
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Prediction of Tissue Damage Using a User-Independent Machine Learning Algorithm vs. Tmax Threshold Maps. CLINICAL AND TRANSLATIONAL NEUROSCIENCE 2021. [DOI: 10.3390/ctn5030021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
(1) Background: To test the accuracy of a fully automated stroke tissue estimation algorithm (FASTER) to predict final lesion volumes in an independent dataset in patients with acute stroke; (2) Methods: Tissue-at-risk prediction was performed in 31 stroke patients presenting with a proximal middle cerebral artery occlusion. FDA-cleared perfusion software using the AHA recommendation for the Tmax threshold delay was tested against a prediction algorithm trained on an independent perfusion software using artificial intelligence (FASTER). Following our endovascular strategy to consequently achieve TICI 3 outcome, we compared patients with complete reperfusion (TICI 3) vs. no reperfusion (TICI 0) after mechanical thrombectomy. Final infarct volume was determined on a routine follow-up MRI or CT at 90 days after the stroke; (3) Results: Compared to the reference standard (infarct volume after 90 days), the decision forest algorithm overestimated the final infarct volume in patients without reperfusion. Underestimation was observed if patients were completely reperfused. In cases where the FDA-cleared segmentation was not interpretable due to improper definitions of the arterial input function, the decision forest provided reliable results; (4) Conclusions: The prediction accuracy of automated tissue estimation depends on (i) success of reperfusion, (ii) infarct size, and (iii) software-related factors introduced by the training sample. A principal advantage of machine learning algorithms is their improved robustness to artifacts in comparison to solely threshold-based model-dependent software. Validation on independent datasets remains a crucial condition for clinical implementations of decision support systems in stroke imaging.
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14
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Hassan MW, Keshk A, El-atey AA, Alfeky E. BRAIN STROKE DETECTION USING TENSOR FACTORIZATION AND MACHINE LEARNING MODELS. INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGIES AND MANAGEMENT RESEARCH 2021; 8:1-12. [DOI: 10.29121/ijetmr.v8.i8.2021.1006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Stroke is one of the foremost common disorders among the elderly. Early detection of stroke from Magnetic Resonance Imaging (MRI) is typically based on the representation method of these images. Representing MRI slices in two dimensional structures (matrices) implies ignoring the dependencies between these slices. Additionally, to combine all features exist in these slices requires more computations and time. However, this results in inexact diagnosis. In this paper, we propose a new tensor-based approach for stroke detection from MRI. The proposed methodology has two phases. In first phase, each patient’s MRI are represented as a tensor. Tensor representations are powerful because they capture the dependencies in high-dimensional data, MRI of patient, which gives more reliable and accurate results. Also, tensor factorization is used as a method for feature extraction and reduction, which improves the performance and accuracy of classifiers. In second phase, these extracted features are used to train support vector machine (SVM) and XGBoost classifiers to classify MRI images into normal and abnormal. The proposed method is assessed with MRI dataset, and the conducted experiments illustrate the efficiency of this approach. It achieves classification accuracy of 98%.
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15
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Faust O, En Wei Koh J, Jahmunah V, Sabut S, Ciaccio EJ, Majid A, Ali A, Lip GYH, Acharya UR. Fusion of Higher Order Spectra and Texture Extraction Methods for Automated Stroke Severity Classification with MRI Images. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:8059. [PMID: 34360349 PMCID: PMC8345794 DOI: 10.3390/ijerph18158059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 07/05/2021] [Accepted: 07/23/2021] [Indexed: 11/18/2022]
Abstract
This paper presents a scientific foundation for automated stroke severity classification. We have constructed and assessed a system which extracts diagnostically relevant information from Magnetic Resonance Imaging (MRI) images. The design was based on 267 images that show the brain from individual subjects after stroke. They were labeled as either Lacunar Syndrome (LACS), Partial Anterior Circulation Syndrome (PACS), or Total Anterior Circulation Stroke (TACS). The labels indicate different physiological processes which manifest themselves in distinct image texture. The processing system was tasked with extracting texture information that could be used to classify a brain MRI image from a stroke survivor into either LACS, PACS, or TACS. We analyzed 6475 features that were obtained with Gray-Level Run Length Matrix (GLRLM), Higher Order Spectra (HOS), as well as a combination of Discrete Wavelet Transform (DWT) and Gray-Level Co-occurrence Matrix (GLCM) methods. The resulting features were ranked based on the p-value extracted with the Analysis Of Variance (ANOVA) algorithm. The ranked features were used to train and test four types of Support Vector Machine (SVM) classification algorithms according to the rules of 10-fold cross-validation. We found that SVM with Radial Basis Function (RBF) kernel achieves: Accuracy (ACC) = 93.62%, Specificity (SPE) = 95.91%, Sensitivity (SEN) = 92.44%, and Dice-score = 0.95. These results indicate that computer aided stroke severity diagnosis support is possible. Such systems might lead to progress in stroke diagnosis by enabling healthcare professionals to improve diagnosis and management of stroke patients with the same resources.
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Affiliation(s)
- Oliver Faust
- Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK
| | - Joel En Wei Koh
- School of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore; (J.E.W.K.); (V.J.); (U.R.A.)
| | - Vicnesh Jahmunah
- School of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore; (J.E.W.K.); (V.J.); (U.R.A.)
| | - Sukant Sabut
- School of Electronics Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha 751024, India;
| | - Edward J. Ciaccio
- Department of Medicine-Cardiology, Columbia University, New York, NY 10027, USA;
| | - Arshad Majid
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield S10 2HQ, UK;
| | - Ali Ali
- Sheffield Teaching Hospitals NIHR Biomedical Research Centre, Sheffield S10 2JF, UK;
| | - Gregory Y. H. Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool L69 7TX, UK;
- Aalborg Thrombosis Research Unit, Department of Clinical Medicine, Aalborg University, 9000 Aalborg, Denmark
| | - U. Rajendra Acharya
- School of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore; (J.E.W.K.); (V.J.); (U.R.A.)
- School of Science and Technology, Singapore University of Social Sciences, 463 Clementi Road, Singapore 599494, Singapore
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto 860-8555, Japan
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16
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Yeo M, Kok HK, Kutaiba N, Maingard J, Thijs V, Tahayori B, Russell J, Jhamb A, Chandra RV, Brooks M, Barras CD, Asadi H. Artificial intelligence in clinical decision support and outcome prediction - applications in stroke. J Med Imaging Radiat Oncol 2021; 65:518-528. [PMID: 34050596 DOI: 10.1111/1754-9485.13193] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 04/29/2021] [Indexed: 01/19/2023]
Abstract
Artificial intelligence (AI) is making a profound impact in healthcare, with the number of AI applications in medicine increasing substantially over the past five years. In acute stroke, it is playing an increasingly important role in clinical decision-making. Contemporary advances have increased the amount of information - both clinical and radiological - which clinicians must consider when managing patients. In the time-critical setting of acute stroke, AI offers the tools to rapidly evaluate and consolidate available information, extracting specific predictions from rich, noisy data. It has been applied to the automatic detection of stroke lesions on imaging and can guide treatment decisions through the prediction of tissue outcomes and long-term functional outcomes. This review examines the current state of AI applications in stroke, exploring their potential to reform stroke care through clinical decision support, as well as the challenges and limitations which must be addressed to facilitate their acceptance and adoption for clinical use.
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Affiliation(s)
- Melissa Yeo
- School of Medicine, University of Melbourne, Melbourne, Victoria, Australia
| | - Hong Kuan Kok
- Interventional Radiology Service, Department of Radiology, Northern Health, Melbourne, Victoria, Australia
- School of Medicine, Faculty of Health, Deakin University, Burwood, Victoria, Australia
| | - Numan Kutaiba
- Department of Radiology, Austin Hospital, Melbourne, Victoria, Australia
| | - Julian Maingard
- School of Medicine, Faculty of Health, Deakin University, Burwood, Victoria, Australia
- Interventional Neuroradiology Unit, Monash Health, Clayton, Victoria, Australia
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
| | - Vincent Thijs
- Stroke Theme, Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
- Department of Neurology, Austin Health, Melbourne, Victoria, Australia
| | - Bahman Tahayori
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
- IBM Research Australia, Melbourne, Victoria, Australia
| | - Jeremy Russell
- Department of Neurosurgery, Austin Hospital, Melbourne, Victoria, Australia
| | - Ashu Jhamb
- Department of Radiology, St Vincent's Hospital, Melbourne, Victoria, Australia
| | - Ronil V Chandra
- Interventional Neuroradiology Unit, Monash Health, Clayton, Victoria, Australia
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
| | - Mark Brooks
- School of Medicine, University of Melbourne, Melbourne, Victoria, Australia
- School of Medicine, Faculty of Health, Deakin University, Burwood, Victoria, Australia
- Stroke Theme, Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
- Interventional Neuroradiology Service, Department of Radiology, Austin Hospital, Melbourne, Victoria, Australia
| | - Christen D Barras
- South Australian Institute of Health and Medical Research, Adelaide, South Australia, Australia
- School of Medicine, The University of Adelaide, Adelaide, South Australia, Australia
| | - Hamed Asadi
- School of Medicine, Faculty of Health, Deakin University, Burwood, Victoria, Australia
- Interventional Neuroradiology Unit, Monash Health, Clayton, Victoria, Australia
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
- Stroke Theme, Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
- Department of Radiology, St Vincent's Hospital, Melbourne, Victoria, Australia
- Interventional Neuroradiology Service, Department of Radiology, Austin Hospital, Melbourne, Victoria, Australia
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17
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Kuang H, Menon BK, Sohn SI, Qiu W. EIS-Net: Segmenting early infarct and scoring ASPECTS simultaneously on non-contrast CT of patients with acute ischemic stroke. Med Image Anal 2021; 70:101984. [PMID: 33676101 DOI: 10.1016/j.media.2021.101984] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 12/14/2020] [Accepted: 01/25/2021] [Indexed: 11/29/2022]
Abstract
Detecting early infarct (EI) plays an essential role in patient selection for reperfusion therapy in the management of acute ischemic stroke (AIS). EI volume at acute or hyper-acute stage can be measured using advanced pre-treatment imaging, such as MRI and CT perfusion. In this study, a novel multi-task learning approach, EIS-Net, is proposed to segment EI and score Alberta Stroke Program Early CT Score (ASPECTS) simultaneously on baseline non-contrast CT (NCCT) scans of AIS patients. The EIS-Net comprises of a 3D triplet convolutional neural network (T-CNN) for EI segmentation and a multi-region classification network for ASPECTS scoring. T-CNN has triple encoders with original NCCT, mirrored NCCT, and atlas as inputs, as well as one decoder. A comparison disparity block (CDB) is designed to extract and enhance image contexts. In the decoder, a multi-level attention gate module (MAGM) is developed to recalibrate the features of the decoder for both segmentation and classification tasks. Evaluations using a high-quality dataset comprising of baseline NCCT and concomitant diffusion weighted MRI (DWI) as reference standard of 260 patients with AIS show that the proposed EIS-Net can accurately segment EI. The EIS-Net segmented EI volume strongly correlates with EI volume on DWI (r=0.919), and the mean difference between the two volumes is 8.5 mL. For ASPECTS scoring, the proposed EIS-Net achieves an intraclass correlation coefficient of 0.78 for total 10-point ASPECTS and a kappa of 0.75 for dichotomized ASPECTS (≤ 4 vs. >4). Both EI segmentation and ASPECTS scoring tasks achieve state-of-the-art performances.
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Affiliation(s)
- Hulin Kuang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China; Department of Clinical Neuroscience, the University of Calgary, Calgary, Alberta, Canada
| | - Bijoy K Menon
- Department of Clinical Neuroscience, the University of Calgary, Calgary, Alberta, Canada; Department of Radiology, the University of Calgary, Calgary, Alberta, Canada
| | - Sung Il Sohn
- Department of Neurology, Keimyung University Dongsan Medical Center, Daegu, South Korea
| | - Wu Qiu
- Department of Clinical Neuroscience, the University of Calgary, Calgary, Alberta, Canada; Department of Radiology, the University of Calgary, Calgary, Alberta, Canada.
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18
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Nowinski WL, Walecki J, Półtorak-Szymczak G, Sklinda K, Mruk B. Ischemic infarct detection, localization, and segmentation in noncontrast CT human brain scans: review of automated methods. PeerJ 2021; 8:e10444. [PMID: 33391867 PMCID: PMC7759129 DOI: 10.7717/peerj.10444] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 11/07/2020] [Indexed: 01/01/2023] Open
Abstract
Noncontrast Computed Tomography (NCCT) of the brain has been the first-line diagnosis for emergency evaluation of acute stroke, so a rapid and automated detection, localization, and/or segmentation of ischemic lesions is of great importance. We provide the state-of-the-art review of methods for automated detection, localization, and/or segmentation of ischemic lesions on NCCT in human brain scans along with their comparison, evaluation, and classification. Twenty-two methods are (1) reviewed and evaluated; (2) grouped into image processing and analysis-based methods (11 methods), brain atlas-based methods (two methods), intensity template-based methods (1 method), Stroke Imaging Marker-based methods (two methods), and Artificial Intelligence-based methods (six methods); and (3) properties of these groups of methods are characterized. A new method classification scheme is proposed as a 2 × 2 matrix with local versus global processing and analysis, and density versus spatial sampling. Future studies are necessary to develop more efficient methods directed toward deep learning methods as well as combining the global methods with a high sampling both in space and density for the merged radiologic and neurologic data.
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Affiliation(s)
- Wieslaw L Nowinski
- John Paul II Center for Virtual Anatomy and Surgical Simulation, University of Cardinal Stefan Wyszynski, Warsaw, Poland
| | - Jerzy Walecki
- Department of Radiology and Diagnostic Imaging, Center of Postgraduate Medical Education, Warsaw, Poland
| | - Gabriela Półtorak-Szymczak
- Department of Radiology and Diagnostic Imaging, Center of Postgraduate Medical Education, Warsaw, Poland
| | - Katarzyna Sklinda
- Department of Radiology and Diagnostic Imaging, Center of Postgraduate Medical Education, Warsaw, Poland
| | - Bartosz Mruk
- Department of Radiology and Diagnostic Imaging, Center of Postgraduate Medical Education, Warsaw, Poland
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19
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Debs N, Cho TH, Rousseau D, Berthezène Y, Buisson M, Eker O, Mechtouff L, Nighoghossian N, Ovize M, Frindel C. Impact of the reperfusion status for predicting the final stroke infarct using deep learning. NEUROIMAGE-CLINICAL 2020; 29:102548. [PMID: 33450521 PMCID: PMC7810765 DOI: 10.1016/j.nicl.2020.102548] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 12/15/2020] [Accepted: 12/20/2020] [Indexed: 01/10/2023]
Abstract
BACKGROUND Predictive maps of the final infarct may help therapeutic decisions in acute ischemic stroke patients. Our objectives were to assess whether integrating the reperfusion status into deep learning models would improve their performance, and to compare them to current clinical prediction methods. METHODS We trained and tested convolutional neural networks (CNNs) to predict the final infarct in acute ischemic stroke patients treated by thrombectomy in our center. When training the CNNs, non-reperfused patients from a non-thrombectomized cohort were added to the training set to increase the size of this group. Baseline diffusion and perfusion-weighted magnetic resonance imaging (MRI) were used as inputs, and the lesion segmented on day-6 MRI served as the ground truth for the final infarct. The cohort was dichotomized into two subsets, reperfused and non-reperfused patients, from which reperfusion status specific CNNs were developed and compared to one another, and to the clinically-used perfusion-diffusion mismatch model. Evaluation metrics included the Dice similarity coefficient (DSC), precision, recall, volumetric similarity, Hausdorff distance and area-under-the-curve (AUC). RESULTS We analyzed 109 patients, including 35 without reperfusion. The highest DSC were achieved in both reperfused and non-reperfused patients (DSC = 0.44 ± 0.25 and 0.47 ± 0.17, respectively) when using the corresponding reperfusion status-specific CNN. CNN-based models achieved higher DSC and AUC values compared to those of perfusion-diffusion mismatch models (reperfused patients: AUC = 0.87 ± 0.13 vs 0.79 ± 0.17, P < 0.001; non-reperfused patients: AUC = 0.81 ± 0.13 vs 0.73 ± 0.14, P < 0.01, in CNN vs perfusion-diffusion mismatch models, respectively). CONCLUSION The performance of deep learning models improved when the reperfusion status was incorporated in their training. CNN-based models outperformed the clinically-used perfusion-diffusion mismatch model. Comparing the predicted infarct in case of successful vs failed reperfusion may help in estimating the treatment effect and guiding therapeutic decisions in selected patients.
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Affiliation(s)
- Noëlie Debs
- CREATIS, CNRS, UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon, Villeurbanne, France.
| | - Tae-Hee Cho
- CREATIS, CNRS, UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon, Villeurbanne, France; Department of Vascular Neurology, Hospices Civils de Lyon, Lyon, France.
| | - David Rousseau
- LARIS, UMR IRHS INRA, Université d'Angers, Angers, France.
| | - Yves Berthezène
- CREATIS, CNRS, UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon, Villeurbanne, France; Department of Neuroradiology, Hospices Civils de Lyon, Lyon, France.
| | - Marielle Buisson
- Department of Cardiology, Clinical Investigation Center, CarMeN INSERM U1060, INRA U1397, INSA Lyon, Université Lyon 1, Hospices Civils de Lyon, Lyon, France.
| | - Omer Eker
- CREATIS, CNRS, UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon, Villeurbanne, France; Department of Neuroradiology, Hospices Civils de Lyon, Lyon, France.
| | - Laura Mechtouff
- Department of Vascular Neurology, Hospices Civils de Lyon, Lyon, France; Department of Cardiology, Clinical Investigation Center, CarMeN INSERM U1060, INRA U1397, INSA Lyon, Université Lyon 1, Hospices Civils de Lyon, Lyon, France.
| | - Norbert Nighoghossian
- CREATIS, CNRS, UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon, Villeurbanne, France; Department of Vascular Neurology, Hospices Civils de Lyon, Lyon, France.
| | - Michel Ovize
- Department of Neuroradiology, Hospices Civils de Lyon, Lyon, France.
| | - Carole Frindel
- CREATIS, CNRS, UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon, Villeurbanne, France.
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20
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Pinto A, Pereira S, Meier R, Wiest R, Alves V, Reyes M, Silva CA. Combining unsupervised and supervised learning for predicting the final stroke lesion. Med Image Anal 2020; 69:101888. [PMID: 33387909 DOI: 10.1016/j.media.2020.101888] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 10/09/2020] [Accepted: 10/22/2020] [Indexed: 10/22/2022]
Abstract
Predicting the final ischaemic stroke lesion provides crucial information regarding the volume of salvageable hypoperfused tissue, which helps physicians in the difficult decision-making process of treatment planning and intervention. Treatment selection is influenced by clinical diagnosis, which requires delineating the stroke lesion, as well as characterising cerebral blood flow dynamics using neuroimaging acquisitions. Nonetheless, predicting the final stroke lesion is an intricate task, due to the variability in lesion size, shape, location and the underlying cerebral haemodynamic processes that occur after the ischaemic stroke takes place. Moreover, since elapsed time between stroke and treatment is related to the loss of brain tissue, assessing and predicting the final stroke lesion needs to be performed in a short period of time, which makes the task even more complex. Therefore, there is a need for automatic methods that predict the final stroke lesion and support physicians in the treatment decision process. We propose a fully automatic deep learning method based on unsupervised and supervised learning to predict the final stroke lesion after 90 days. Our aim is to predict the final stroke lesion location and extent, taking into account the underlying cerebral blood flow dynamics that can influence the prediction. To achieve this, we propose a two-branch Restricted Boltzmann Machine, which provides specialized data-driven features from different sets of standard parametric Magnetic Resonance Imaging maps. These data-driven feature maps are then combined with the parametric Magnetic Resonance Imaging maps, and fed to a Convolutional and Recurrent Neural Network architecture. We evaluated our proposal on the publicly available ISLES 2017 testing dataset, reaching a Dice score of 0.38, Hausdorff Distance of 29.21 mm, and Average Symmetric Surface Distance of 5.52 mm.
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Affiliation(s)
- Adriano Pinto
- Center MEMS of University of Minho, Campus of Azurém, Guimarães 4800-058 Portugal; Center Algoritmi, University of Minho, Braga, Portugal.
| | - Sérgio Pereira
- Center MEMS of University of Minho, Campus of Azurém, Guimarães 4800-058 Portugal; Center Algoritmi, University of Minho, Braga, Portugal
| | - Raphael Meier
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Bern University Hospital, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Bern University Hospital, Switzerland
| | - Victor Alves
- Center Algoritmi, University of Minho, Braga, Portugal
| | - Mauricio Reyes
- Healthcare Imaging A.I., Insel Data Science Center, Bern University Hospital, Switzerland
| | - Carlos A Silva
- Center MEMS of University of Minho, Campus of Azurém, Guimarães 4800-058 Portugal.
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Separability of Acute Cerebral Infarction Lesions in CT Based Radiomics: Toward Artificial Intelligence-Assisted Diagnosis. BIOMED RESEARCH INTERNATIONAL 2020; 2020:8864756. [PMID: 33274231 PMCID: PMC7683107 DOI: 10.1155/2020/8864756] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 11/02/2020] [Accepted: 11/03/2020] [Indexed: 12/29/2022]
Abstract
This study aims at analyzing the separability of acute cerebral infarction lesions which were invisible in CT. 38 patients, who were diagnosed with acute cerebral infarction and performed both CT and MRI, and 18 patients, who had no positive finding in either CT or MRI, were enrolled. Comparative studies were performed on lesion and symmetrical regions, normal brain and symmetrical regions, lesion, and normal brain regions. MRI was reconstructed and affine transformed to obtain accurate lesion position of CT. Radiomic features and information gain were introduced to capture efficient features. Finally, 10 classifiers were established with selected features to evaluate the effectiveness of analysis. 1301 radiomic features were extracted from candidate regions after registration. For lesion and their symmetrical regions, there were 280 features with information gain greater than 0.1 and 2 features with information gain greater than 0.3. The average classification accuracy was 0.6467, and the best classification accuracy was 0.7748. For normal brain and their symmetrical regions, there were 176 features with information gain greater than 0.1, 1 feature with information gain greater than 0.2. The average classification accuracy was 0.5414, and the best classification accuracy was 0.6782. For normal brain and lesions, there were 501 features with information gain greater than 0.1 and 1 feature with information gain greater than 0.5. The average classification accuracy was 0.7480, and the best classification accuracy was 0.8694. In conclusion, the study captured significant features correlated with acute cerebral infarction and confirmed the separability of acute lesions in CT, which established foundation for further artificial intelligence-assisted CT diagnosis.
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22
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Krishan A, Mittal D. Ensembled liver cancer detection and classification using CT images. Proc Inst Mech Eng H 2020; 235:232-244. [PMID: 33183141 DOI: 10.1177/0954411920971888] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Computed tomography (CT) images are commonly used to diagnose liver disease. It is sometimes very difficult to comment on the type, category and level of the tumor, even for experienced radiologists, directly from the CT image, due to the varying intensities. In recent years, it has been important to design and develop computer-assisted imaging techniques to help doctors/physicians improve their diagnosis. The proposed work is to detect the presence of a tumor region in the liver and classify the different stages of the tumor from CT images. CT images of the liver have been classified between normal and tumor classes. In addition, CT images of the tumor have been classified between Hepato Cellular Carcinoma (HCC) and Metastases (MET). The performance of six different classifiers was evaluated on different parameters. The accuracy achieved for different classifiers varies between 98.39% and 100% for tumor identification and between 76.38% and 87.01% for tumor classification. To further, improve performance, a multi-level ensemble model is developed to detect a tumor (liver cancer) and to classify between HCC and MET using features extracted from CT images. The k-fold cross-validation (CV) is also used to justify the robustness of the classifiers. Compared to the individual classifier, the multi-level ensemble model achieved high accuracy in both the detection and classification of different tumors. This study demonstrates automated tumor characterization based on liver CT images and will assist the radiologist in detecting and classifying different types of tumors at a very early stage.
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Affiliation(s)
- Abhay Krishan
- Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, Patiala, India
| | - Deepti Mittal
- Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, Patiala, India
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Kuang H, Menon BK, Qiu W. Automated stroke lesion segmentation in non-contrast CT scans using dense multi-path contextual generative adversarial network. Phys Med Biol 2020; 65:215013. [PMID: 32604080 DOI: 10.1088/1361-6560/aba166] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Stroke lesion volume is a key radiologic measurement in assessing prognosis of acute ischemic stroke (AIS) patients. The aim of this paper is to develop an automated segmentation method for accurately segmenting follow-up ischemic and hemorrhagic lesion from multislice non-contrast CT (NCCT) volumes of AIS patients. This paper proposes a 2D dense multi-path contextual generative adversarial network (MPC-GAN) where a dense multi-path 2D U-Net is utilized as the generator and a discriminator network is applied to regularize the generator. Contextual information (i.e. bilateral intensity difference, distance map and lesion location probability) are input into the generator and discriminator. The proposed method is validated separately on follow-up NCCT volumes of 60 patients with ischemic infarcts and NCCT volumes of 70 patients with hemorrhages. Quantitative results demonstrated that the proposed MPC-GAN method obtained a Dice coefficient (DC) of 70.6% for ischemic infarct segmentation and a DC of 76.5% for hemorrhage segmentation compared with manual segmented lesions, outperforming several benchmark methods. Additional volumetric analyses demonstrated that the MPC-GAN segmented lesion volume correlated well with manual measurements (Pearson correlation coefficients were 0.926 and 0.927 for ischemic infarcts and hemorrhages, respectively). The proposed MPC-GAN method can accurately segment ischemic infarcts and hemorrhages from NCCT volumes of AIS patients.
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Affiliation(s)
- Hulin Kuang
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, T2N 2T9 Canada
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Osama S, Zafar K, Sadiq MU. Predicting Clinical Outcome in Acute Ischemic Stroke Using Parallel Multi-parametric Feature Embedded Siamese Network. Diagnostics (Basel) 2020; 10:E858. [PMID: 33105609 PMCID: PMC7690444 DOI: 10.3390/diagnostics10110858] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Revised: 10/16/2020] [Accepted: 10/19/2020] [Indexed: 11/16/2022] Open
Abstract
Stroke is the second leading cause of death and disability worldwide, with ischemic stroke as the most common type. The preferred diagnostic procedure at the acute stage is the acquisition of multi-parametric magnetic resonance imaging (MRI). This type of imaging not only detects and locates the stroke lesion, but also provides the blood flow dynamics that helps clinicians in assessing the risks and benefits of reperfusion therapies. However, evaluating the outcome of these risky therapies beforehand is a complicated task due to the variability of lesion location, size, shape, and cerebral hemodynamics involved. Though the fully automated model for predicting treatment outcomes using multi-parametric imaging would be highly valuable in clinical settings, MRI datasets acquired at the acute stage are mostly scarce and suffer high class imbalance. In this paper, parallel multi-parametric feature embedded siamese network (PMFE-SN) is proposed that can learn with few samples and can handle skewness in multi-parametric MRI data. Moreover, five suitable evaluation metrics that are insensitive to imbalance are defined for this problem. The results show that PMFE-SN not only outperforms other state-of-the-art techniques in all these metrics but also can predict the class with a small number of samples, as well as the class with high number of samples. An accuracy of 0.67 on leave one cross out testing has been achieved with only two samples (minority class) for training and accuracy of 0.61 with the highest number of samples (majority class). In comparison, state-of-the-art using hand crafted features has 0 accuracy for minority class and 0.33 accuracy for majority class.
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Affiliation(s)
| | - Kashif Zafar
- Department of Computer Science, National University of Computing and Emerging Sciences, 852-B Milaad St, Block B Faisal Town, Lahore 54000, Pakistan; (S.O.); (M.U.S.)
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Clèrigues A, Valverde S, Bernal J, Freixenet J, Oliver A, Lladó X. Acute and sub-acute stroke lesion segmentation from multimodal MRI. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 194:105521. [PMID: 32434099 DOI: 10.1016/j.cmpb.2020.105521] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 03/30/2020] [Accepted: 04/23/2020] [Indexed: 05/25/2023]
Abstract
BACKGROUND AND OBJECTIVE Acute stroke lesion segmentation tasks are of great clinical interest as they can help doctors make better informed time-critical treatment decisions. Magnetic resonance imaging (MRI) is time demanding but can provide images that are considered the gold standard for diagnosis. Automated stroke lesion segmentation can provide with an estimate of the location and volume of the lesioned tissue, which can help in the clinical practice to better assess and evaluate the risks of each treatment. METHODS We propose a deep learning methodology for acute and sub-acute stroke lesion segmentation using multimodal MR imaging. We pre-process the data to facilitate learning features based on the symmetry of brain hemispheres. The issue of class imbalance is tackled using small patches with a balanced training patch sampling strategy and a dynamically weighted loss function. Moreover, a combination of whole patch predictions, using a U-Net based CNN architecture, and high degree of overlapping patches reduces the need for additional post-processing. RESULTS The proposed method is evaluated using two public datasets from the 2015 Ischemic Stroke Lesion Segmentation challenge (ISLES 2015). These involve the tasks of sub-acute stroke lesion segmentation (SISS) and acute stroke penumbra estimation (SPES) from multiple diffusion, perfusion and anatomical MRI modalities. The performance is compared against state-of-the-art methods with a blind online testing set evaluation on each of the challenges. At the time of submitting this manuscript, our approach is the first method in the online rankings for the SISS (DSC=0.59 ± 0.31) and SPES sub-tasks (DSC=0.84 ± 0.10). When compared with the rest of submitted strategies, we achieve top rank performance with a lower Hausdorff distance. CONCLUSIONS Better segmentation results are obtained by leveraging the anatomy and pathophysiology of acute stroke lesions and using a combined approach to minimize the effects of class imbalance. The same training procedure is used for both tasks, showing the proposed methodology can generalize well enough to deal with different unrelated tasks and imaging modalities without hyper-parameter tuning. In order to promote the reproducibility of our results, a public version of the proposed method has been released to the scientific community.
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Affiliation(s)
- Albert Clèrigues
- Institute of Computer Vision and Robotics, University of Girona, P-IV, Campus Montilivi, 17003 Girona Spain.
| | - Sergi Valverde
- Institute of Computer Vision and Robotics, University of Girona, P-IV, Campus Montilivi, 17003 Girona Spain
| | - Jose Bernal
- Institute of Computer Vision and Robotics, University of Girona, P-IV, Campus Montilivi, 17003 Girona Spain
| | - Jordi Freixenet
- Institute of Computer Vision and Robotics, University of Girona, P-IV, Campus Montilivi, 17003 Girona Spain
| | - Arnau Oliver
- Institute of Computer Vision and Robotics, University of Girona, P-IV, Campus Montilivi, 17003 Girona Spain
| | - Xavier Lladó
- Institute of Computer Vision and Robotics, University of Girona, P-IV, Campus Montilivi, 17003 Girona Spain
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Wang G, Song T, Dong Q, Cui M, Huang N, Zhang S. Automatic ischemic stroke lesion segmentation from computed tomography perfusion images by image synthesis and attention-based deep neural networks. Med Image Anal 2020; 65:101787. [DOI: 10.1016/j.media.2020.101787] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Revised: 07/04/2020] [Accepted: 07/16/2020] [Indexed: 12/24/2022]
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Kumar A, Upadhyay N, Ghosal P, Chowdhury T, Das D, Mukherjee A, Nandi D. CSNet: A new DeepNet framework for ischemic stroke lesion segmentation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 193:105524. [PMID: 32417618 DOI: 10.1016/j.cmpb.2020.105524] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 04/07/2020] [Accepted: 04/27/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVES Acute stroke lesion segmentation is of paramount importance as it can aid medical personnel to render a quicker diagnosis and administer consequent treatment. Automation of this task is technically exacting due to the variegated appearance of lesions and their dynamic development, medical discrepancies, unavailability of datasets, and the requirement of several MRI modalities for imaging. In this paper, we propose a composite deep learning model primarily based on the self-similar fractal networks and the U-Net model for performing acute stroke diagnosis tasks automatically to assist as well as expedite the decision-making process of medical practitioners. METHODS We put forth a new deep learning architecture, the Classifier-Segmenter network (CSNet), involving a hybrid training strategy with a self-similar (fractal) U-Net model, explicitly designed to perform the task of segmentation. In fractal networks, the underlying design strategy is based on the repetitive generation of self-similar fractals in place of residual connections. The U-Net model exploits both spatial as well as semantic information along with parameter sharing for a faster and efficient training process. In this new architecture, we exploit the benefits of both by combining them into one hybrid training scheme and developing the concept of a cascaded architecture, which further enhances the model's accuracy by removing redundant parts from the Segmenter's input. Lastly, a voting mechanism has been employed to further enhance the overall segmentation accuracy. RESULTS The performance of the proposed architecture has been scrutinized against the existing state-of-the-art deep learning architectures applied to various biomedical image processing tasks by submission on the publicly accessible web platform provided by the MICCAI Ischemic Stroke Lesion Segmentation (ISLES) challenge. The experimental results demonstrate the superiority of the proposed method when compared to similar submitted strategies, both qualitatively and quantitatively in terms of some of the well known evaluation metrics, such as Accuracy, Dice-Coefficient, Recall, and Precision. CONCLUSIONS We believe that our method may find use as a handy tool for doctors to identify the location and extent of irreversibly damaged brain tissue, which is said to be a critical part of the decision-making process in case of an acute stroke.
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Affiliation(s)
- Amish Kumar
- Department of Computer Science and Engineering, National Institute of Technoloy Durgapur - 713209, West Bengal, India
| | - Neha Upadhyay
- Department of Computer Science and Engineering, National Institute of Technoloy Durgapur - 713209, West Bengal, India
| | - Palash Ghosal
- Department of Computer Science and Engineering, National Institute of Technoloy Durgapur - 713209, West Bengal, India
| | - Tamal Chowdhury
- Department of Electronics and Communication Engineering, National Institute of Technoloy Durgapur - 713209, West Bengal, India
| | - Dipayan Das
- Department of Electronics and Communication Engineering, National Institute of Technoloy Durgapur - 713209, West Bengal, India
| | - Amritendu Mukherjee
- Department of Interventional Radiology, Rashid Hospital, Dubai-4545, United Arab Emirates
| | - Debashis Nandi
- Department of Computer Science and Engineering, National Institute of Technoloy Durgapur - 713209, West Bengal, India.
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Rachmadi MF, Valdés-Hernández MDC, Makin S, Wardlaw J, Komura T. Automatic spatial estimation of white matter hyperintensities evolution in brain MRI using disease evolution predictor deep neural networks. Med Image Anal 2020; 63:101712. [PMID: 32428823 PMCID: PMC7294240 DOI: 10.1016/j.media.2020.101712] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 04/10/2020] [Accepted: 04/20/2020] [Indexed: 11/24/2022]
Abstract
Previous studies have indicated that white matter hyperintensities (WMH), the main radiological feature of small vessel disease, may evolve (i.e., shrink, grow) or stay stable over a period of time. Predicting these changes are challenging because it involves some unknown clinical risk factors that leads to a non-deterministic prediction task. In this study, we propose a deep learning model to predict the evolution of WMH from baseline to follow-up (i.e., 1-year later), namely "Disease Evolution Predictor" (DEP) model, which can be adjusted to become a non-deterministic model. The DEP model receives a baseline image as input and produces a map called "Disease Evolution Map" (DEM), which represents the evolution of WMH from baseline to follow-up. Two DEP models are proposed, namely DEP-UResNet and DEP-GAN, which are representatives of the supervised (i.e., need expert-generated manual labels to generate the output) and unsupervised (i.e., do not require manual labels produced by experts) deep learning algorithms respectively. To simulate the non-deterministic and unknown parameters involved in WMH evolution, we modulate a Gaussian noise array to the DEP model as auxiliary input. This forces the DEP model to imitate a wider spectrum of alternatives in the prediction results. The alternatives of using other types of auxiliary input instead, such as baseline WMH and stroke lesion loads are also proposed and tested. Based on our experiments, the fully supervised machine learning scheme DEP-UResNet regularly performed better than the DEP-GAN which works in principle without using any expert-generated label (i.e., unsupervised). However, a semi-supervised DEP-GAN model, which uses probability maps produced by a supervised segmentation method in the learning process, yielded similar performances to the DEP-UResNet and performed best in the clinical evaluation. Furthermore, an ablation study showed that an auxiliary input, especially the Gaussian noise, improved the performance of DEP models compared to DEP models that lacked the auxiliary input regardless of the model's architecture. To the best of our knowledge, this is the first extensive study on modelling WMH evolution using deep learning algorithms, which deals with the non-deterministic nature of WMH evolution.
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Affiliation(s)
- Muhammad Febrian Rachmadi
- School of Informatics, University of Edinburgh, Edinburgh, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
| | | | - Stephen Makin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Centre for Rural Health, University of Aberdeen, UK
| | - Joanna Wardlaw
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Taku Komura
- School of Informatics, University of Edinburgh, Edinburgh, UK
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Nael K. Detection of Acute Infarction on Non-Contrast-enhanced CT: Closing the Gap with MRI via Machine Learning. Radiology 2020; 294:645-646. [PMID: 31990628 DOI: 10.1148/radiol.2020192703] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Kambiz Nael
- From the Department of Radiology, Icahn School of Medicine at Mount Sinai; and Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 757 Westwood Plaza, Suite 1621, Los Angeles, CA, 90095-7532
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Qiu W, Kuang H, Teleg E, Ospel JM, Sohn SI, Almekhlafi M, Goyal M, Hill MD, Demchuk AM, Menon BK. Machine Learning for Detecting Early Infarction in Acute Stroke with Non-Contrast-enhanced CT. Radiology 2020; 294:638-644. [PMID: 31990267 DOI: 10.1148/radiol.2020191193] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Identifying the presence and extent of infarcted brain tissue at baseline plays a crucial role in the treatment of patients with acute ischemic stroke (AIS). Patients with extensive infarction are unlikely to benefit from thrombolysis or thrombectomy procedures. Purpose To develop an automated approach to detect and quantitate infarction by using non-contrast-enhanced CT scans in patients with AIS. Materials and Methods Non-contrast-enhanced CT images in patients with AIS (<6 hours from symptom onset to CT) who also underwent diffusion-weighted (DW) MRI within 1 hour after AIS were obtained from May 2004 to July 2009 and were included in this retrospective study. Ischemic lesions manually contoured on DW MRI scans were used as the reference standard. An automatic segmentation approach involving machine learning (ML) was developed to detect infarction. Randomly selected nonenhanced CT images from 157 patients with the lesion labels manually contoured on DW MRI scans were used to train and validate the ML model; the remaining 100 patients independent of the derivation cohort were used for testing. The ML algorithm was quantitatively compared with the reference standard (DW MRI) by using Bland-Altman plots and Pearson correlation. Results In 100 patients in the testing data set (median age, 69 years; interquartile range [IQR]: 59-76 years; 59 men), baseline non-contrast-enhanced CT was performed within a median time of 48 minutes from symptom onset (IQR, 27-93 minutes); baseline MRI was performed a median of 38 minutes (IQR, 24-48 minutes) later. The algorithm-detected lesion volume correlated with the reference standard of expert-contoured lesion volume in acute DW MRI scans (r = 0.76, P < .001). The mean difference between the algorithm-segmented volume (median, 15 mL; IQR, 9-38 mL) and the DW MRI volume (median, 19 mL; IQR, 5-43 mL) was 11 mL (P = .89). Conclusion A machine learning approach for segmentation of infarction on non-contrast-enhanced CT images in patients with acute ischemic stroke showed good agreement with stroke volume on diffusion-weighted MRI scans. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Nael in this issue.
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Affiliation(s)
- Wu Qiu
- From the Calgary Stroke Program, Departments of Clinical Neurosciences (W.Q., H.K., E.T., J.M.O., M.G., M.D.H., A.M.D., B.K.M.), Radiology (M.G., M.D.H., A.M.D., B.K.M.), and Community Health Sciences (M.D.H., B.K.M.), University of Calgary, 239 Strathridge Pl SW, Calgary, AB, Canada T3H 4J2; Hotchkiss Brain Institute, Calgary, Alberta, Canada (M.G., M.D.H., A.M.D., B.K.M.), Department of Neurology, Keimyung University, Daegu, South Korea (S.I.S.); and Division of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland (J.M.O.)
| | - Hulin Kuang
- From the Calgary Stroke Program, Departments of Clinical Neurosciences (W.Q., H.K., E.T., J.M.O., M.G., M.D.H., A.M.D., B.K.M.), Radiology (M.G., M.D.H., A.M.D., B.K.M.), and Community Health Sciences (M.D.H., B.K.M.), University of Calgary, 239 Strathridge Pl SW, Calgary, AB, Canada T3H 4J2; Hotchkiss Brain Institute, Calgary, Alberta, Canada (M.G., M.D.H., A.M.D., B.K.M.), Department of Neurology, Keimyung University, Daegu, South Korea (S.I.S.); and Division of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland (J.M.O.)
| | - Ericka Teleg
- From the Calgary Stroke Program, Departments of Clinical Neurosciences (W.Q., H.K., E.T., J.M.O., M.G., M.D.H., A.M.D., B.K.M.), Radiology (M.G., M.D.H., A.M.D., B.K.M.), and Community Health Sciences (M.D.H., B.K.M.), University of Calgary, 239 Strathridge Pl SW, Calgary, AB, Canada T3H 4J2; Hotchkiss Brain Institute, Calgary, Alberta, Canada (M.G., M.D.H., A.M.D., B.K.M.), Department of Neurology, Keimyung University, Daegu, South Korea (S.I.S.); and Division of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland (J.M.O.)
| | - Johanna M Ospel
- From the Calgary Stroke Program, Departments of Clinical Neurosciences (W.Q., H.K., E.T., J.M.O., M.G., M.D.H., A.M.D., B.K.M.), Radiology (M.G., M.D.H., A.M.D., B.K.M.), and Community Health Sciences (M.D.H., B.K.M.), University of Calgary, 239 Strathridge Pl SW, Calgary, AB, Canada T3H 4J2; Hotchkiss Brain Institute, Calgary, Alberta, Canada (M.G., M.D.H., A.M.D., B.K.M.), Department of Neurology, Keimyung University, Daegu, South Korea (S.I.S.); and Division of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland (J.M.O.)
| | - Sung Il Sohn
- From the Calgary Stroke Program, Departments of Clinical Neurosciences (W.Q., H.K., E.T., J.M.O., M.G., M.D.H., A.M.D., B.K.M.), Radiology (M.G., M.D.H., A.M.D., B.K.M.), and Community Health Sciences (M.D.H., B.K.M.), University of Calgary, 239 Strathridge Pl SW, Calgary, AB, Canada T3H 4J2; Hotchkiss Brain Institute, Calgary, Alberta, Canada (M.G., M.D.H., A.M.D., B.K.M.), Department of Neurology, Keimyung University, Daegu, South Korea (S.I.S.); and Division of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland (J.M.O.)
| | - Mohammed Almekhlafi
- From the Calgary Stroke Program, Departments of Clinical Neurosciences (W.Q., H.K., E.T., J.M.O., M.G., M.D.H., A.M.D., B.K.M.), Radiology (M.G., M.D.H., A.M.D., B.K.M.), and Community Health Sciences (M.D.H., B.K.M.), University of Calgary, 239 Strathridge Pl SW, Calgary, AB, Canada T3H 4J2; Hotchkiss Brain Institute, Calgary, Alberta, Canada (M.G., M.D.H., A.M.D., B.K.M.), Department of Neurology, Keimyung University, Daegu, South Korea (S.I.S.); and Division of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland (J.M.O.)
| | - Mayank Goyal
- From the Calgary Stroke Program, Departments of Clinical Neurosciences (W.Q., H.K., E.T., J.M.O., M.G., M.D.H., A.M.D., B.K.M.), Radiology (M.G., M.D.H., A.M.D., B.K.M.), and Community Health Sciences (M.D.H., B.K.M.), University of Calgary, 239 Strathridge Pl SW, Calgary, AB, Canada T3H 4J2; Hotchkiss Brain Institute, Calgary, Alberta, Canada (M.G., M.D.H., A.M.D., B.K.M.), Department of Neurology, Keimyung University, Daegu, South Korea (S.I.S.); and Division of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland (J.M.O.)
| | - Michael D Hill
- From the Calgary Stroke Program, Departments of Clinical Neurosciences (W.Q., H.K., E.T., J.M.O., M.G., M.D.H., A.M.D., B.K.M.), Radiology (M.G., M.D.H., A.M.D., B.K.M.), and Community Health Sciences (M.D.H., B.K.M.), University of Calgary, 239 Strathridge Pl SW, Calgary, AB, Canada T3H 4J2; Hotchkiss Brain Institute, Calgary, Alberta, Canada (M.G., M.D.H., A.M.D., B.K.M.), Department of Neurology, Keimyung University, Daegu, South Korea (S.I.S.); and Division of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland (J.M.O.)
| | - Andrew M Demchuk
- From the Calgary Stroke Program, Departments of Clinical Neurosciences (W.Q., H.K., E.T., J.M.O., M.G., M.D.H., A.M.D., B.K.M.), Radiology (M.G., M.D.H., A.M.D., B.K.M.), and Community Health Sciences (M.D.H., B.K.M.), University of Calgary, 239 Strathridge Pl SW, Calgary, AB, Canada T3H 4J2; Hotchkiss Brain Institute, Calgary, Alberta, Canada (M.G., M.D.H., A.M.D., B.K.M.), Department of Neurology, Keimyung University, Daegu, South Korea (S.I.S.); and Division of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland (J.M.O.)
| | - Bijoy K Menon
- From the Calgary Stroke Program, Departments of Clinical Neurosciences (W.Q., H.K., E.T., J.M.O., M.G., M.D.H., A.M.D., B.K.M.), Radiology (M.G., M.D.H., A.M.D., B.K.M.), and Community Health Sciences (M.D.H., B.K.M.), University of Calgary, 239 Strathridge Pl SW, Calgary, AB, Canada T3H 4J2; Hotchkiss Brain Institute, Calgary, Alberta, Canada (M.G., M.D.H., A.M.D., B.K.M.), Department of Neurology, Keimyung University, Daegu, South Korea (S.I.S.); and Division of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland (J.M.O.)
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Melingi SB, Vijayalakshmi V. A Hybrid Approach for Sub-Acute Ischemic Stroke Lesion Segmentation Using Random Decision Forest and Gravitational Search Algorithm. Curr Med Imaging 2020; 15:170-183. [PMID: 31975663 DOI: 10.2174/1573405614666180209150338] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Revised: 01/17/2018] [Accepted: 01/29/2018] [Indexed: 11/22/2022]
Abstract
BACKGROUND The sub-acute ischemic stroke is the most basic illnesses reason for death on the planet. We evaluate the impact of segmentation technique during the time of breaking down the capacities of the cerebrum. OBJECTIVE The main objective of this paper is to segment the ischemic stroke lesions in Magnetic Resonance (MR) images in the presence of other pathologies like neurological disorder, encephalopathy, brain damage, Multiple sclerosis (MS). METHODS In this paper, we utilize a hybrid way to deal with segment the ischemic stroke from alternate pathologies in magnetic resonance (MR) images utilizing Random Decision Forest (RDF) and Gravitational Search Algorithm (GSA). The RDF approach is an effective machine learning approach. RESULTS The RDF strategy joins two parameters; they are; the number of trees in the forest and the number of leaves per tree; it runs quickly and proficiently when dealing with vast data. The GSA algorithm is utilized to optimize the RDF data for choosing the best number of trees and the number of leaves per tree in the forest. CONCLUSION This paper provides a new hybrid GSA-RDF classifier technique to segment the ischemic stroke lesions in MR images. The experimental results demonstrate that the proposed technique has the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Bias Error (MBE) ranges are 16.5485 %, 7.2654 %, and 2.4585 %individually. The proposed RDF-GSA algorithm has better precision and execution when compared with the existing ischemic stroke segmentation method.
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Affiliation(s)
- Sunil Babu Melingi
- Department of Electronics and Communication Engineering, Pondicherry Engineering College (PEC), Puducherry, India
| | - V Vijayalakshmi
- Department of Electronics and Communication Engineering, Pondicherry Engineering College (PEC), Puducherry, India
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Gupta A, Vupputuri A, Ghosh N. Delineation of Ischemic Core and Penumbra Volumes from MRI using MSNet Architecture. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:6730-6733. [PMID: 31947385 DOI: 10.1109/embc.2019.8857708] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Medical image analysis tasks like segmentation and detection of injury involving manual interventions usually suffers from high inter-observer variabilities. To carry them out efficiently, various deep neural networks have been proposed recently as they provide much higher and reliable performance than the traditional image processing and manual segmentation methods. Non-invasive and robust quantification of salvageable tissue in acute ischemic stroke i.e., the ischemic penumbra, is critical for interventional stroke therapy. This paper proposes a Multi- Sequence Network (MSNet) architecture for this task. In this architecture, the information from multiple sequences are combined for identification and segmentation of core and penumbra (salvageable tissue) regions of ischemic stroke lesions and was tested on multisequence MRI ischemic lesion dataset of ISLES015. Performance of the proposed architecture, in terms of dice similarity coefficient, sensitivity and specificity are found to be 0.68, 0.805 and 0.99 respectively for the core of the lesion and 0.69, 0.949 and 0.964 respectively for the penumbra region.
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Vupputuri A, Ashwal S, Tsao B, Ghosh N. Ischemic stroke segmentation in multi-sequence MRI by symmetry determined superpixel based hierarchical clustering. Comput Biol Med 2019; 116:103536. [PMID: 31783255 DOI: 10.1016/j.compbiomed.2019.103536] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 11/07/2019] [Accepted: 11/07/2019] [Indexed: 02/02/2023]
Abstract
Automated estimation of ischemic stroke evolution across different brain anatomical regions has immense potential to revolutionize stroke treatment. Multi-sequence Magnetic Resonance Imaging (MRI) techniques provide information to characterize abnormal tissues based on their anatomy and physical properties. Asymmetry of the right and left hemispheres of the brain is an important cue for abnormality estimation but using it alone is susceptible to occasional error due to self-asymmetry of the brain. A precise estimate of the symmetry axis is therefore essential for accurate asymmetry identification, which holds the key to the proposed method. The proposed symmetry determined superpixel based hierarchical clustering (SSHC) method initially estimates the lesion from inter-hemispheric asymmetry. This asymmetry further determines the thresholding parameter for hierarchically clustering the superpixels leading to an automated and accurate lesion delineation. A multi-sequence MRI based pipeline also combines the estimations from individual sequences. SSHC is evaluated on different sequences of the Loma Linda University (LLU) dataset with 26 patients and the Ischemic Stroke Lesion Segmentation (ISLES'15) dataset with 28 patients. SSHC eliminates the need for manual determination of threshold for combining the superpixel clusters and is more reliable as it derives the information from the quick estimation of asymmetry. SSHC outperforms the state-of-the-art resulting in a high Dice similarity score of 0.704±0.27 and a recall of 0.85±0.01 which are 6% and 35% respectively higher than the challenge winning method. SSHC thus demonstrates a promising potential in the automated detection of (sub-)acute adult ischemic stroke.
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Affiliation(s)
- Anusha Vupputuri
- Department of Electrical Engineering, Indian Institute of Technology, Kharagpur, 721302, India.
| | - Stephen Ashwal
- Department of Pediatrics, Loma Linda University, Loma Linda, CA, 92354, USA.
| | - Bryan Tsao
- Department of Neurology, Loma Linda University, Loma Linda, CA, 92354, USA.
| | - Nirmalya Ghosh
- Department of Electrical Engineering, Indian Institute of Technology, Kharagpur, 721302, India.
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A deep supervised approach for ischemic lesion segmentation from multimodal MRI using Fully Convolutional Network. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.105685] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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35
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Loughnan R, Lorca-Puls DL, Gajardo-Vidal A, Espejo-Videla V, Gillebert CR, Mantini D, Price CJ, Hope TMH. Generalizing post-stroke prognoses from research data to clinical data. Neuroimage Clin 2019; 24:102005. [PMID: 31670072 PMCID: PMC6831940 DOI: 10.1016/j.nicl.2019.102005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Revised: 09/10/2019] [Accepted: 09/14/2019] [Indexed: 11/29/2022]
Abstract
Around a third of stroke survivors suffer from acquired language disorders (aphasia), but current medicine cannot predict whether or when they might recover. Prognostic research in this area increasingly draws on datasets associating structural brain imaging data with outcome scores for ever-larger samples of stroke patients. The aim is to learn brain-behaviour trends from these data, and generalize those trends to predict outcomes for new patients. The practical significance of this work depends on the expected breadth of that generalization. Here, we show that these models can generalize across countries and native languages (from British patients tested in English to Chilean patients tested in Spanish), across neuroimaging technology (from MRI to CT), and from scans collected months or years after stroke for research purposes, to scans collected days or weeks after stroke for clinical purposes. Our results suggest one important confound, in attempting to generalize from research data to clinical data, is the delay between scan acquisition and language assessment. This delay is typically small for research data, where scans and assessments are often acquired contemporaneously. But the most natural, clinical application of these predictions will employ acute prognostic factors to predict much longer-term outcomes. We mitigated this confound by projecting the clinical patients' lesions from the time when their scans were acquired, to the time when their language abilities were assessed; with this projection in place, there was strong evidence that prognoses derived from research data generalized equally well to research and clinical data. These results encourage attention to the confounding role that lesion growth may play in other types of lesion-symptom analysis.
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Affiliation(s)
- Robert Loughnan
- Department of Cognitive Science, University of California, San Diego, USA
| | - Diego L Lorca-Puls
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, London WC1N 3AR, UK; Department of Speech, Language and Hearing Sciences, Faculty of Medicine, Universidad de Concepcion, Concepcion, Chile
| | - Andrea Gajardo-Vidal
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, London WC1N 3AR, UK; Department of Speech, Language and Hearing Sciences, Faculty of Medicine, Universidad de Concepcion, Concepcion, Chile; Faculty of Health Sciences, Universidad del Desarrollo, Concepcion, Chile
| | - Valeria Espejo-Videla
- Department of Speech, Language and Hearing Sciences, Faculty of Medicine, Universidad de Concepcion, Concepcion, Chile
| | - Céline R Gillebert
- Department of Experimental Psychology, University of Oxford, Oxford, UK; Department of Brain and Cognition, University of Leuven, Leuven, Belgium
| | - Dante Mantini
- Department of Experimental Psychology, University of Oxford, Oxford, UK; Research Center for Movement Control and Neuroplasticity, University of Leuven, Leuven, Belgium; Functional Neuroimaging Laboratory, IRCCS San Camillo Hospital Foundation, Venice, Italy
| | - Cathy J Price
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, London WC1N 3AR, UK
| | - Thomas M H Hope
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, London WC1N 3AR, UK.
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36
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Combination of hand-crafted and unsupervised learned features for ischemic stroke lesion detection from Magnetic Resonance Images. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2019.01.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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37
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Pszczolkowski S, Law ZK, Gallagher RG, Meng D, Swienton DJ, Morgan PS, Bath PM, Sprigg N, Dineen RA. Automated segmentation of haematoma and perihaematomal oedema in MRI of acute spontaneous intracerebral haemorrhage. Comput Biol Med 2019; 106:126-139. [PMID: 30711800 PMCID: PMC6382492 DOI: 10.1016/j.compbiomed.2019.01.022] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 01/24/2019] [Accepted: 01/24/2019] [Indexed: 11/19/2022]
Abstract
BACKGROUND Spontaneous intracerebral haemorrhage (SICH) is a common condition with high morbidity and mortality. Segmentation of haematoma and perihaematoma oedema on medical images provides quantitative outcome measures for clinical trials and may provide important markers of prognosis in people with SICH. METHODS We take advantage of improved contrast seen on magnetic resonance (MR) images of patients with acute and early subacute SICH and introduce an automated algorithm for haematoma and oedema segmentation from these images. To our knowledge, there is no previously proposed segmentation technique for SICH that utilises MR images directly. The method is based on shape and intensity analysis for haematoma segmentation and voxel-wise dynamic thresholding of hyper-intensities for oedema segmentation. RESULTS Using Dice scores to measure segmentation overlaps between labellings yielded by the proposed algorithm and five different expert raters on 18 patients, we observe that our technique achieves overlap scores that are very similar to those obtained by pairwise expert rater comparison. A further comparison between the proposed method and a state-of-the-art Deep Learning segmentation on a separate set of 32 manually annotated subjects confirms the proposed method can achieve comparable results with very mild computational burden and in a completely training-free and unsupervised way. CONCLUSION Our technique can be a computationally light and effective way to automatically delineate haematoma and oedema extent directly from MR images. Thus, with increasing use of MR images clinically after intracerebral haemorrhage this technique has the potential to inform clinical practice in the future.
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Affiliation(s)
- Stefan Pszczolkowski
- Stroke Trials Unit, Division of Clinical Neuroscience, University of Nottingham, UK; Radiological Sciences, Division of Clinical Neuroscience, University of Nottingham, UK.
| | - Zhe K Law
- Stroke Trials Unit, Division of Clinical Neuroscience, University of Nottingham, UK; Department of Medicine, National University of Malaysia, Malaysia.
| | - Rebecca G Gallagher
- Department of Neuroradiology, Nottingham University Hospitals, Queen's Medical Centre, Nottingham, UK; Department of Radiology, Royal Derby Hospital, Derby, UK.
| | - Dewen Meng
- Radiological Sciences, Division of Clinical Neuroscience, University of Nottingham, UK.
| | - David J Swienton
- Department of Neuroradiology, Nottingham University Hospitals, Queen's Medical Centre, Nottingham, UK; Imaging Department, Leicester Royal Infirmary, Leicester, UK.
| | - Paul S Morgan
- Medical Physics and Clinical Engineering, Nottingham University Hospitals, Queen's Medical Centre, Nottingham, UK.
| | - Philip M Bath
- Stroke Trials Unit, Division of Clinical Neuroscience, University of Nottingham, UK.
| | - Nikola Sprigg
- Stroke Trials Unit, Division of Clinical Neuroscience, University of Nottingham, UK.
| | - Rob A Dineen
- Radiological Sciences, Division of Clinical Neuroscience, University of Nottingham, UK; NIHR Nottingham BRC, UK.
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38
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Liu L, Chen S, Zhang F, Wu FX, Pan Y, Wang J. Deep convolutional neural network for automatically segmenting acute ischemic stroke lesion in multi-modality MRI. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04096-x] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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39
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de Oliveira JR, Camargo SEA, de Oliveira LD. Rosmarinus officinalis L. (rosemary) as therapeutic and prophylactic agent. J Biomed Sci 2019; 26:5. [PMID: 30621719 PMCID: PMC6325740 DOI: 10.1186/s12929-019-0499-8] [Citation(s) in RCA: 121] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 01/02/2019] [Indexed: 12/22/2022] Open
Abstract
Rosmarinus officinalis L. (rosemary) is a medicinal plant native to the Mediterranean region and cultivated around the world. Besides the therapeutic purpose, it is commonly used as a condiment and food preservative. R. officinalis L. is constituted by bioactive molecules, the phytocompounds, responsible for implement several pharmacological activities, such as anti-inflammatory, antioxidant, antimicrobial, antiproliferative, antitumor and protective, inhibitory and attenuating activities. Thus, in vivo and in vitro studies were presented in this Review, approaching the therapeutic and prophylactic effects of R. officinalis L. on some physiological disorders caused by biochemical, chemical or biological agents. In this way, methodology, mechanisms, results, and conclusions were described. The main objective of this study was showing that plant products could be equivalent to the available medicines.
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Affiliation(s)
- Jonatas Rafael de Oliveira
- Departamento de Biociências e Diagnóstico Bucal, Instituto de Ciência e Tecnologia, Universidade Estadual Paulista (UNESP), Av. Engenheiro Francisco José Longo, 777 - Jardim São Dimas, São José dos Campos, SP, CEP 12245-000, Brazil.
| | | | - Luciane Dias de Oliveira
- Departamento de Biociências e Diagnóstico Bucal, Instituto de Ciência e Tecnologia, Universidade Estadual Paulista (UNESP), Av. Engenheiro Francisco José Longo, 777 - Jardim São Dimas, São José dos Campos, SP, CEP 12245-000, Brazil
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40
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Ischemic Stroke Lesion Segmentation in CT Perfusion Scans Using Pyramid Pooling and Focal Loss. BRAINLESION: GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES 2019. [DOI: 10.1007/978-3-030-11723-8_36] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
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41
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Dolz J, Ben Ayed I, Desrosiers C. Dense Multi-path U-Net for Ischemic Stroke Lesion Segmentation in Multiple Image Modalities. BRAINLESION: GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES 2019. [DOI: 10.1007/978-3-030-11723-8_27] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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42
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Pinto A, Mckinley R, Alves V, Wiest R, Silva CA, Reyes M. Stroke Lesion Outcome Prediction Based on MRI Imaging Combined With Clinical Information. Front Neurol 2018; 9:1060. [PMID: 30568631 PMCID: PMC6290552 DOI: 10.3389/fneur.2018.01060] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Accepted: 11/21/2018] [Indexed: 02/02/2023] Open
Abstract
In developed countries, the second leading cause of death is stroke, which has the ischemic stroke as the most common type. The preferred diagnosis procedure involves the acquisition of multi-modal Magnetic Resonance Imaging. Besides detecting and locating the stroke lesion, Magnetic Resonance Imaging captures blood flow dynamics that guides the physician in evaluating the risks and benefits of the reperfusion procedure. However, the decision process is an intricate task due to the variability of lesion size, shape, and location, as well as the complexity of the underlying cerebral hemodynamic process. Therefore, an automatic method that predicts the stroke lesion outcome, at a 3-month follow-up, would provide an important support to the physicians' decision process. In this work, we propose an automatic deep learning-based method for stroke lesion outcome prediction. Our main contribution resides in the combination of multi-modal Magnetic Resonance Imaging maps with non-imaging clinical meta-data: the thrombolysis in cerebral infarction scale, which categorizes the success of recanalization, achieved through mechanical thrombectomy. In our proposal, this clinical information is considered at two levels. First, at a population level by embedding the clinical information in a custom loss function used during training of our deep learning architecture. Second, at a patient-level through an extra input channel of the neural network used at testing time for a given patient case. By merging imaging with non-imaging clinical information, we aim to obtain a model aware of the principal and collateral blood flow dynamics for cases where there is no perfusion beyond the point of occlusion and for cases where the perfusion is complete after the occlusion point.
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Affiliation(s)
- Adriano Pinto
- CMEMS-UMinho Research Unit, University of Minho, Guimarães, Portugal.,Centro Algoritmi, University of Minho, Braga, Portugal
| | - Richard Mckinley
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Victor Alves
- Centro Algoritmi, University of Minho, Braga, Portugal
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Carlos A Silva
- CMEMS-UMinho Research Unit, University of Minho, Guimarães, Portugal
| | - Mauricio Reyes
- Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland
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43
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Giacalone M, Rasti P, Debs N, Frindel C, Cho TH, Grenier E, Rousseau D. Local spatio-temporal encoding of raw perfusion MRI for the prediction of final lesion in stroke. Med Image Anal 2018; 50:117-126. [PMID: 30268970 DOI: 10.1016/j.media.2018.08.008] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Revised: 07/28/2018] [Accepted: 08/31/2018] [Indexed: 10/28/2022]
Abstract
We address the medical image analysis issue of predicting the final lesion in stroke from early perfusion magnetic resonance imaging. The classical processing approach for the dynamical perfusion images consists in a temporal deconvolution to improve the temporal signals associated with each voxel before performing prediction. We demonstrate here the value of exploiting directly the raw perfusion data by encoding the local environment of each voxel as a spatio-temporal texture, with an observation scale larger than the voxel. As a first illustration for this approach, the textures are characterized with local binary patterns and the classification is performed using a standard support vector machine (SVM). This simple machine learning classification scheme demonstrates results with 95% accuracy on average while working only raw perfusion data. We discuss the influence of the observation scale and evaluate the interest of using post-processed perfusion data with this approach.
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Affiliation(s)
- Mathilde Giacalone
- CREATIS, CNRS UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon Bât. Blaise Pascal, 7 avenue Jean Capelle, Villeurbanne 69621, France
| | - Pejman Rasti
- LARIS, UMR IRHS INRA, Université d'Angers 62 avenue Notre Dame du Lac, Angers 49000, France
| | - Noelie Debs
- CREATIS, CNRS UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon Bât. Blaise Pascal, 7 avenue Jean Capelle, Villeurbanne 69621, France
| | - Carole Frindel
- CREATIS, CNRS UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon Bât. Blaise Pascal, 7 avenue Jean Capelle, Villeurbanne 69621, France
| | - Tae-Hee Cho
- CREATIS, CNRS UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon Bât. Blaise Pascal, 7 avenue Jean Capelle, Villeurbanne 69621, France
| | - Emmanuel Grenier
- ENS-Lyon, UMR CNRS 5669 'UMPA', and INRIA Alpes, project NUMED, Lyon F-69364, France
| | - David Rousseau
- LARIS, UMR IRHS INRA, Université d'Angers 62 avenue Notre Dame du Lac, Angers 49000, France.
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44
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Ting FF, Sim KS, Lim CP. Case-control comparison brain lesion segmentation for early infarct detection. Comput Med Imaging Graph 2018; 69:82-95. [PMID: 30219737 DOI: 10.1016/j.compmedimag.2018.08.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Revised: 08/24/2018] [Accepted: 08/24/2018] [Indexed: 11/16/2022]
Abstract
Computed Tomography (CT) images are widely used for the identification of abnormal brain tissues following infarct and hemorrhage of a stroke. The treatment of this medical condition mainly depends on doctors' experience. While manual lesion delineation by medical doctors is currently considered as the standard approach, it is time-consuming and dependent on each doctor's expertise and experience. In this study, a case-control comparison brain lesion segmentation (CCBLS) method is proposed to segment the region pertaining to brain injury by comparing the voxel intensity of CT images between control subjects and stroke patients. The method is able to segment the brain lesion from the stacked CT images automatically without prior knowledge of the location or the presence of the lesion. The aim is to reduce medical doctors' burden and assist them in making an accurate diagnosis. A case study with 300 sets of CT images from control subjects and stroke patients is conducted. Comparing with other existing methods, the outcome ascertains the effectiveness of the proposed method in detecting brain infarct of stroke patients.
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Affiliation(s)
- Fung Fung Ting
- Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450, Melaka, Malaysia.
| | - Kok Swee Sim
- Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450, Melaka, Malaysia.
| | - Chee Peng Lim
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, Victoria, Australia.
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45
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Rebouças EDS, Marques RCP, Braga AM, Oliveira SAF, de Albuquerque VHC, Rebouças Filho PP. New level set approach based on Parzen estimation for stroke segmentation in skull CT images. Soft comput 2018. [DOI: 10.1007/s00500-018-3491-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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46
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Praveen G, Agrawal A, Sundaram P, Sardesai S. Ischemic stroke lesion segmentation using stacked sparse autoencoder. Comput Biol Med 2018; 99:38-52. [DOI: 10.1016/j.compbiomed.2018.05.027] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Revised: 05/23/2018] [Accepted: 05/29/2018] [Indexed: 11/26/2022]
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47
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Guerrero R, Qin C, Oktay O, Bowles C, Chen L, Joules R, Wolz R, Valdés-Hernández MC, Dickie DA, Wardlaw J, Rueckert D. White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks. NEUROIMAGE-CLINICAL 2017. [PMID: 29527496 PMCID: PMC5842732 DOI: 10.1016/j.nicl.2017.12.022] [Citation(s) in RCA: 104] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
White matter hyperintensities (WMH) are a feature of sporadic small vessel disease also frequently observed in magnetic resonance images (MRI) of healthy elderly subjects. The accurate assessment of WMH burden is of crucial importance for epidemiological studies to determine association between WMHs, cognitive and clinical data; their causes, and the effects of new treatments in randomized trials. The manual delineation of WMHs is a very tedious, costly and time consuming process, that needs to be carried out by an expert annotator (e.g. a trained image analyst or radiologist). The problem of WMH delineation is further complicated by the fact that other pathological features (i.e. stroke lesions) often also appear as hyperintense regions. Recently, several automated methods aiming to tackle the challenges of WMH segmentation have been proposed. Most of these methods have been specifically developed to segment WMH in MRI but cannot differentiate between WMHs and strokes. Other methods, capable of distinguishing between different pathologies in brain MRI, are not designed with simultaneous WMH and stroke segmentation in mind. Therefore, a task specific, reliable, fully automated method that can segment and differentiate between these two pathological manifestations on MRI has not yet been fully identified. In this work we propose to use a convolutional neural network (CNN) that is able to segment hyperintensities and differentiate between WMHs and stroke lesions. Specifically, we aim to distinguish between WMH pathologies from those caused by stroke lesions due to either cortical, large or small subcortical infarcts. The proposed fully convolutional CNN architecture, called uResNet, that comprised an analysis path, that gradually learns low and high level features, followed by a synthesis path, that gradually combines and up-samples the low and high level features into a class likelihood semantic segmentation. Quantitatively, the proposed CNN architecture is shown to outperform other well established and state-of-the-art algorithms in terms of overlap with manual expert annotations. Clinically, the extracted WMH volumes were found to correlate better with the Fazekas visual rating score than competing methods or the expert-annotated volumes. Additionally, a comparison of the associations found between clinical risk-factors and the WMH volumes generated by the proposed method, was found to be in line with the associations found with the expert-annotated volumes. Robust, fully automatic white matter hyperintensity and stroke lesion segmentation and differentiation A novel patch sampling strategy used during CNN training that avoids the introduction of erroneous locality assumptions Improved segmentation accuracy in terms of Dice scores when compared to well established state-of-the-art methods
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Affiliation(s)
- R Guerrero
- Department of Computing, Imperial College London, UK.
| | - C Qin
- Department of Computing, Imperial College London, UK
| | - O Oktay
- Department of Computing, Imperial College London, UK
| | - C Bowles
- Department of Computing, Imperial College London, UK
| | - L Chen
- Department of Computing, Imperial College London, UK
| | | | - R Wolz
- IXICO plc., UK; Department of Computing, Imperial College London, UK
| | - M C Valdés-Hernández
- UK Dementia Research Institute at The University of Edinburgh, Edinburgh Medical School, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - D A Dickie
- UK Dementia Research Institute at The University of Edinburgh, Edinburgh Medical School, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - J Wardlaw
- UK Dementia Research Institute at The University of Edinburgh, Edinburgh Medical School, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - D Rueckert
- Department of Computing, Imperial College London, UK
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48
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Karthik R, Menaka R. Computer-aided detection and characterization of stroke lesion – a short review on the current state-of-the art methods. IMAGING SCIENCE JOURNAL 2017. [DOI: 10.1080/13682199.2017.1370879] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Affiliation(s)
- R. Karthik
- School of Electronics Engineering, VIT University, Chennai, India
| | - R. Menaka
- School of Electronics Engineering, VIT University, Chennai, India
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49
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McKinley R, Häni L, Gralla J, El-Koussy M, Bauer S, Arnold M, Fischer U, Jung S, Mattmann K, Reyes M, Wiest R. Fully automated stroke tissue estimation using random forest classifiers (FASTER). J Cereb Blood Flow Metab 2017; 37:2728-2741. [PMID: 27798267 PMCID: PMC5536784 DOI: 10.1177/0271678x16674221] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Several clinical trials have recently proven the efficacy of mechanical thrombectomy for treating ischemic stroke, within a six-hour window for therapy. To move beyond treatment windows and toward personalized risk assessment, it is essential to accurately identify the extent of tissue-at-risk ("penumbra"). We introduce a fully automated method to estimate the penumbra volume using multimodal MRI (diffusion-weighted imaging, a T2w- and T1w contrast-enhanced sequence, and dynamic susceptibility contrast perfusion MRI). The method estimates tissue-at-risk by predicting tissue damage in the case of both persistent occlusion and of complete recanalization. When applied to 19 test cases with a thrombolysis in cerebral infarction grading of 1-2a, mean overestimation of final lesion volume was 30 ml, compared with 121 ml for manually corrected thresholding. Predicted tissue-at-risk volume was positively correlated with final lesion volume ( p < 0.05). We conclude that prediction of tissue damage in the event of either persistent occlusion or immediate and complete recanalization, from spatial features derived from MRI, provides a substantial improvement beyond predefined thresholds. It may serve as an alternative method for identifying tissue-at-risk that may aid in treatment selection in ischemic stroke.
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Affiliation(s)
- Richard McKinley
- 1 Support Center for Advanced Neuroimaging (SCAN), Institute for Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland
| | - Levin Häni
- 1 Support Center for Advanced Neuroimaging (SCAN), Institute for Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland
| | - Jan Gralla
- 1 Support Center for Advanced Neuroimaging (SCAN), Institute for Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland
| | - M El-Koussy
- 1 Support Center for Advanced Neuroimaging (SCAN), Institute for Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland
| | - S Bauer
- 2 Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland
| | - M Arnold
- 3 Department of Neurology, Inselspital, University of Bern, Bern, Switzerland
| | - U Fischer
- 3 Department of Neurology, Inselspital, University of Bern, Bern, Switzerland
| | - S Jung
- 3 Department of Neurology, Inselspital, University of Bern, Bern, Switzerland
| | - Kaspar Mattmann
- 1 Support Center for Advanced Neuroimaging (SCAN), Institute for Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland
| | - Mauricio Reyes
- 2 Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland
| | - Roland Wiest
- 1 Support Center for Advanced Neuroimaging (SCAN), Institute for Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland
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Kanchana R, Menaka R. A novel approach for characterisation of ischaemic stroke lesion using histogram bin-based segmentation and gray level co-occurrence matrix features. IMAGING SCIENCE JOURNAL 2017. [DOI: 10.1080/13682199.2017.1295586] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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